{"id":"W2888773612","doi":"10.1080/15481603.2018.1513444","title":"Monitoring surface changes in discontinuous permafrost terrain using small baseline SAR interferometry, object-based classification, and geological features: a case study from Mayo, Yukon Territory, Canada","year":2018,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre For Cold Ocean Resources Engineering; Memorial University of Newfoundland","funders":"","keywords":"Permafrost; Interferometric synthetic aperture radar; Terrain; Geology; Remote sensing; GNSS augmentation; Land cover; Baseline (sea); Vegetation (pathology); Synthetic aperture radar; Interferometry; Arctic; Subsidence; Physical geography; Geodesy; Cartography; Geomorphology; Geography; Global Positioning System; Land use","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001721415,0.0004177301,0.0002570831,0.0009612257,0.001359454,0.0007823797,0.0006796748,0.000436145,0.0002946255],"category_scores_gemma":[0.0003744915,0.0001741349,0.000252837,0.002555531,0.000645294,0.0001946705,0.0003878453,0.0002674272,0.00006729575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006695387,"about_ca_system_score_gemma":0.007216573,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9733708,"about_ca_topic_score_gemma":0.9920363,"domain_scores_codex":[0.9997522,0.0000144469,0.0000102213,0.00004571701,0.00009411733,0.00008323674],"domain_scores_gemma":[0.9997332,0.00002634047,0.000021837,0.00001284199,0.0001555779,0.00005028857],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000215596,0.0003389137,0.9067014,0.0001708515,0.000166728,0.01157233,0.004052912,0.008625424,0.02580904,0.0003897045,0.001612724,0.04034418],"study_design_scores_gemma":[0.00001761443,0.00004715609,0.9793744,0.00002104366,0.00005127227,0.0005348908,0.005575489,0.01155333,0.00136992,0.00004688829,0.001379635,0.00002830341],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982086,0.0001138385,0.0002942958,0.00004823284,0.000002195521,0.00004447449,0.0003723337,0.00001414384,0.0009018789],"genre_scores_gemma":[0.9972088,0.0001843802,0.001324617,0.00002676985,0.000001821664,0.00001214234,0.0005003994,0.000005696266,0.0007355235],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02662921,"threshold_uncertainty_score":0.053572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05946677061716515,"score_gpt":0.2739050385330868,"score_spread":0.2144382679159216,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}